Skip to Main content Skip to Navigation
Journal articles

Unsupervised dense crowd detection by multiscale texture analysis

Abstract : This study introduces a totally unsupervised method for the detection and location of dense crowds in images without context-awareness. With the perspective of setting up fully autonomous video-surveillance systems, automatic detection and location of crowds is a crucial step that is going to point which areas of the image have to be analyzed. After retrieving multiscale texture-related feature vectors from the image, a binary classification is conducted to determine which parts of the image belong to the crowd and which to the background. The algorithm presented can be operated on images without any prior knowledge of any kind and is totally unsupervised.
Complete list of metadatas

https://hal.archives-ouvertes.fr/hal-00904210
Contributor : Nicolas Courty <>
Submitted on : Thursday, November 14, 2013 - 9:43:29 AM
Last modification on : Friday, March 6, 2020 - 4:40:03 PM
Document(s) archivé(s) le : Saturday, February 15, 2014 - 4:30:32 AM

File

UnsupervisedDenseCrowdDetectio...
Files produced by the author(s)

Identifiers

  • HAL Id : hal-00904210, version 1

Citation

Fagette Antoine, Nicolas Courty, Daniel Racoceanu, Jean-Yves Dufour. Unsupervised dense crowd detection by multiscale texture analysis. Pattern Recognition Letters, Elsevier, 2013, pp.1-27. ⟨hal-00904210⟩

Share

Metrics

Record views

933

Files downloads

671